Clinical rehabilitation therapists have been challenged, if not
mandated, to use standardized assessments as part of clinical practice.
Educational accreditation standards and practice frameworks for clinical
rehabilitation disciplines such as physical and occupational therapy
require that students learn and use standardized assessments [1-2]. More
recently, the growing demand for evidence-based practice carries with it
an implicit mandate to use reliable and valid standardized assessments
as the basis of clinical decision making [3-6].

In spite of this foundational training and best-practice focus,
standardized assessments are used infrequently. For example, a
multicenter retrospective review of medical charts showed that only 13
percent of 248 individuals with stroke were assessed for unilateral
neglect with standardized assessments as recommended by practice
guidelines [7]. A recent survey of 253 Canadian occupational therapists
revealed that only 27 percent reported using standardized assessments of
unilateral spatial neglect [8], and a qualitative interview with 12
Swedish occupational therapists working with individuals with brain
injury revealed that the therapists were reluctant to use standardized
assessments [9]. In a survey of 300 Canadian physiotherapists conducted
by Abrams et al., 90 percent agreed that "health professionals
should monitor the outcome of their treatment using reliable and valid
tools," yet less than 30 percent reported routine use of
standardized assessments [10]. Furthermore, Kay et al. reported that in
spite of concerted educational efforts to motivate therapists to use
published outcomes scales, 6 years later, less than half of the
therapists incorporated published scales into practice [11]. There is
also an inherent dissatisfaction with instruments that have widespread
use. For example, in the 102 European stroke rehabilitation facilities
surveyed by Torenbeek et al., while the majority of therapists used
published scales, such as the Barthel Index or Functional Independence
Measure (FIM[TM]), more than 90 percent of the respondents reported
dissatisfaction with these instruments [12].

For good reasons, we continue to promote the advancement of
standardized assessment tools in evidence-based clinical rehabilitation
[6]. Standardized assessments allow comparisons against norms,
objectively document the effectiveness of interventions, and promote the
translation of clinical research into clinical practice. The validity of
rehabilitation constructs within the scientific community will likely
depend on the quality of the tools used to measure these constructs
[13]. Rigorous assessments are undeniably the key to the advancement of
rehabilitation practice and science.

Clearly, standardized assessments have a critical role in clinical
professions, and yet therapists are challenged in incorporating these
assessments into day-to-day clinical practice. While educators,
researchers, and professional organizations continue to criticize
therapists for not using standardized assessments, we may need to
contemplate whether or not standardized assessments have any immediate
benefit to the practicing therapist. The critical question may be
"What has my standardized assessment done for me (the therapist)
lately?" In order to better understand the plight of standardized
assessment in practice, we need to reevaluate the assessment process and
the role of measurement in this process.

Every measure begins with a qualitative experience [14-16]. The
clinical evaluation incorporates in-depth patient interviews (e.g.,
narratives, ethnographies), observations of behaviors (e.g.,
developmental milestones, performance of self-care or instrumental
activities of daily living tasks), and informal observations of patient
participation (e.g., interacting with peers, community integration). It
is the qualitative features of the therapist-patient interaction that
lead to real-time interpretation of the patient's status and the
formulation of a treatment plan tailored to the unique needs of the
individual and his or her context. While standardized assessments
provide a means to quantify some or all of the assessment process, many
assessments do relatively little to immediately inform the therapist
beyond the qualitative interaction. While administrators, healthcare
accrediting agencies, payers, and of course, researchers use numbers to
make critical clinical, financial, and scientific decisions,
unfortunately, the numbers often add little value to the immediate
clinical reasoning process.

The restricted use of assessment instruments in clinical practice
may be a function of the measurement model underlying the development of
these instruments, that is, classical test theory (CTT). CTT explains
the observed score into two components, the true score and error score
[17]. The majority of investigations using this measurement model are at
the observed-score level (e.g., interrater reliability of the observed
scores across raters or predictive validity of the observed scores
relative to a gold standard measure). In essence, this model provides an
understanding of the construct (e.g., upper-limb functioning) at the
level of the whole instrument. Furthermore, the observed scores are
assessment (test) and sample dependent. That is, an individual's
observed score depends on whether he or she takes an easy or hard test,
and reliability and validity values are highly dependent on the
characteristics of the sample [18].

Item response theory (IRT) models may provide a window to extend
the applicability of assessments in clinical practice. In contrast to
CTT, IRT focuses on the item rather than the test. While extensive
reviews of IRT exist [19-21], critical to this study is the following
characteristic of all IRT models: the attempt to measure person ability
(often referred to as a latent trait) relative to item difficulty. In
contrast to CTT, the values of person ability and item difficulty are
sample independent [22]. In the simplest of the IRT models, the Rasch
model, there is a probabilistic relationship of person ability to item
difficulty, with individuals of low ability having a high probability of
failing or getting low ratings on both the easy and hard items of an
assessment and individuals of high ability having higher probability
(than individuals with low ability) of passing or getting high ratings
on harder items and a very high probability of passing or getting high
ratings on easy items [23]. This relationship of item difficulty and
person ability forms the basis of generating patient evaluation forms
called keyforms that can provide immediate, useful information to the
therapist.

Linacre introduced the keyform as an instantaneous measure for the
cognitive construct of the FIM [24]. This form is generated from the
"General Keyforms" output table produced from Rasch analysis
using the Winsteps software program (Winsteps; Chicago, Illinois) [25].
The form looks similar to a survey questionnaire, with the items of the
survey on one side of the form and numbers corresponding to the rating
scale of each item placed on the other side of the form. While the
keyform is similar to a survey questionnaire, it has two additional
features. First, the items of the assessment are ordered on the basis of
Rasch item-difficulty calibrations. The items progress from the easiest
items on the bottom of the form to the hardest items on the top of the
form. Second, the ratings for the items are not lined up in straight
vertical columns as on a typical survey. Instead each rating for each
item is associated with a difficulty calibration. Item ratings
stair-step from left to right to correspond with increasing amounts of
person ability required to perform more difficult items.

Linacre with the FIM cognitive scale [24] and, later, Kielhofner et
al. with the Occupational Performance History Interview-II (OPHI-II)
[26] demonstrated how these keyforms could be used for evaluating
patients and immediately deriving Rasch linear measures during the
assessment process. This was accomplished by circling ratings (for each
item) corresponding to patients' performance (i.e., on the items of
the FIM keyform) or interview responses (i.e., on the items of the
OPHI-II keyform). Then, by drawing a vertical line "through the
bulk of the items" [24] and intersecting the x-axis, the evaluator
could immediately determine the Rasch person-ability measure of the
patient. Linacre and Kielhofner et al. showed that the conversion of raw
scores to interval measures could be derived without specialized
software programs and statistical expertise because the keyform is
generated from previous analysis of data and can be scored in real time.
Similarly, Bode et al. developed a self-scoring key (keyform) for the
Galveston Orientation and Amnesia Test to identify extraordinary
response patterns [27]. Like Linacre and Kielhofner et al., Bode et al.
recommended use of the keyform to generate instantaneous interval
measures.

A further step, beyond instantaneous measurement, is the
application of the keyform to clinical treatment planning. Critical to
this step is the acknowledgement that the items on an instrument
represent amounts of the construct under observation. That is, the items
reflect an item-difficulty hierarchical structure. Avery et al. clearly
demonstrate the item-difficulty structure in their presentation of their
Gross Motor Ability Estimator for the Gross Motor Function Measure-66
(GMFM-66) [28]. Because childhood motor development proceeds along
predictable development milestones (e.g., head control precedes sitting,
which precedes crawling, which precedes walking), the structure of the
keyform acquires meaning. The researchers, using a keyform, present the
example of a child scoring 60/100 on the GMFM-66 [28]. This child has
consistent ratings of 3 (can complete the movement task) for items such
as "lifting head to midline," "sit and lowers to
prone," and "stands with arms free." In addition, this
child has consistent ratings of 0 (cannot complete the movement task)
for items such as "jump forward 30 cm," "up steps with no
rail," and "hop 10 times left foot." The "transition
zone" is where the child scores a 2 (partially completes the
movement task) or 1 (initiates the movement task) on items such as
"high kneeling to stand," "up 4 steps holding rail,"
and "walk between lines." This zone represents the emerging
motor ability skills of the child. Items at this level are at the
"just right challenge" level; for items far below this level,
the child has a high probability of succeeding, and for items far above
this level, the child has a high probability of failing.

A recent study of the Fugl-Meyer Assessment-Upper Extremity
(FMA-UE) offers an opportunity to create a keyform to assist
neurorehabilitation therapists in planning treatment aimed at improving
upper-limb motor function in persons with stroke [29]. This assessment
of stroke-related upper-limb motor impairment was chosen for
investigation because it is reliable and valid and often used in
rehabilitation research studies [30-32]. The 33 items of the assessment
are scored on a 3-point rating scale: 0 = unable to perform, 1 = partial
ability to perform, and 2 = near normal ability to perform [33].
Importantly, for the purposes of the present article, the FMA-UE is
potentially useful for rehabilitation therapists interested in
documenting upper-limb motor recovery during therapy.

Woodbury et al. applied Rasch analysis to 512 participants'
responses to the 33 FMA-UE items [29]. The results led to a modified
30-item FMA-UE, which was shown to be a unidimensional measure of
voluntary upper-limb motor ability. Furthermore, the results arranged
the FMAUE items along a hierarchical continuum according to difficulty
(i.e., from least to most difficult). The item-difficulty hierarchy is a
visual representation of the overall construct the assessment is
intended to measure and describes a progression of this construct, i.e.,
defines a continuum of behaviors representing poststroke upper-limb
motor recovery. The item-difficulty hierarchy challenged the traditional
motor control conceptual framework underlying the FMAUE. That is,
recovery of poststroke upper-limb movement did not proceed in a strict
synergistic-to-isolated and reflexive-to-integrated sequence as would be
expected by the traditional framework. Instead, the Rasch-generated
item-difficulty hierarchy was consistent with contemporary motor control
evidence that suggests an individual's ability to perform a given
arm or hand movement is a dynamic interaction between neural factors and
the task-specific difficulty of the movement.

The Rasch analysis of this sample produced a keyform. Importantly,
the FMA-UE item-difficulty hierarchy was consistent across 98 percent of
a 512-person sample at a single time point and was invariant in a
377-person sample across two testing occasions [29,34]. These findings
strongly suggest that the FMA-UE keyform can be applied to a larger
population of individuals with stroke.

The purpose of this article is to extend previous research by
demonstrating how the FMA-UE keyform can be used to inform treatment
planning. The present study will show how patient performance documented
on a keyform can be immediately used in combination with other
patient-specific information to set goals and plan treatment.

METHODS

The keyform scoring form presented in this article was generated in
a previous published analysis of FMA-UE data obtained from 512
individuals, aged 69.8 [+ or -] 11.1 years. These individuals were
between 0 and 145 days from their first mildly to moderately severe
cortical stroke. Detailed sample demographics are presented elsewhere
[29]. This keyform scoring form is illustrated in Figure 1. FMA-UE items
are listed to the right of the figure in descending difficulty order
(top to bottom). The rating scale for each item (0, 1, and 2) is to the
left of the figure. As can be seen, the rating scale stair-steps from
the left to the right. Easier items (bottom of the figure) have ratings
to the far left of the figure, and harder items (top of the figure) have
ratings close to the right of the figure. This stair-stepping
corresponds to the difficulty of each item. That is, the ratings for
each item are placed relative to the measurement scale (in log-odd units
[logits]) at the bottom of the figure, which estimates the
patient's upper-limb motor ability. For example, for elbow flexion,
the rating of 1 corresponds to approximately -2.0 logits, while for
wrist circumduction the rating of 1 corresponds to approximately 1.0
logits. The logit is an interval-level unit of measurement that
represents the log-odds ratio of the probability that an individual will
successfully or unsuccessfully accomplish an item at a particular part
of the rating scale [23].

In order to demonstrate the application of the keyform for clinical
practice, we used the keyform scoring form constructed in the Woodbury
et al. Rasch analysis [29] to display FMA-UE data from three patients
with stroke enrolled in a separate prospective research study in our
laboratory. A total of 55 participants were recruited from the database
of the Brain Rehabilitation Research Center, a Department of Veterans
Affairs Rehabilitation Research and Development Center of Excellence.
Subjects were included in the study if they (1) had experienced a
single, unilateral ischemic stroke at least 3 months prior; (2)
demonstrated passive range of motion in affected shoulder, elbow, and
wrist within functional limits; and (3) were 18 to 90 years of age.
Potential subjects were excluded if they were unable to understand
three-step directions; had a demonstrated orthopedic condition, pain, or
impaired corrected vision that would alter the kinematics of reaching;
or had experienced a brain stem or cerebellar stroke. All participants
were administered the FMA-UE by trained evaluators according to
standardized procedures outlined by Duncan et al. [30]. The sample was
divided into three categories of upper-limb motor impairment (mild,
moderate, severe) based on the aggregate FMA-UE score. Commonly used
FMA-UE cutoff scores defined each category: 0 to 20 severe, 21 to 50
moderate, and 51 to 66 mild [33]. For the purposes of this article, one
participant was randomly selected from each impairment level.

RESULTS

Data from the three randomly selected individuals is displayed in
Figures 2-4. The participant's ratings for each item were circled
on the keyform. Figure 2, representing data from a patient with severe
upper-limb motor impairment (FMA-UE score 19/60), shows near normal
performance on the easiest items (bottom of figure), with progressively
poorer performance as the items become more difficult. That is, the
patient receives a 2 (near normal performance) on the four easiest items
(elbow flexion, shoulder adduction with internal rotation, finger mass
flexion, and scapular elevation) and a mixture of 0, 1, or 2 ratings
(unable to perform, partial performance, and near normal performance,
respectively) on the next 14 items (e.g., forearm pronation, elbow
extension, and pronation/ supination with elbow at 90[degrees]), and a 0
(unable to perform) on the 12 most difficult items (e.g., shoulder
flexion to 180[degrees], hook grasp, and wrist circumduction). The area
boxed by the dashed line in Figure 2 illustrates the transition zone, in
which the patient's performance fluctuates between unable to
perform (rating = 0), partial performance (rating = 1), and near normal
performance (rating = 2).

[FIGURE 1 OMITTED]

It is important to note that decisions about where to draw the
transition zone boundaries (i.e., edges of the dotted box) are based on
the pattern of the patient's unique responses to the items. The
boundary reflects the point at which a patient begins to transition from
one rating scale category to the next rating scale category. Since the
rating of any item is based on a probability, the edges of the
transition zone are equivocal. However, the overall response pattern
provides the necessary information for treatment planning.

Figures 3-4 present data from patients with moderate and mild
upper-limb motor impairment, respectively (Figure 3: FMA-UE score 36/60,
and Figure 4: FMA-UE score 56/60). Relative to the "severe"
patient (Figure 2), the individual with moderate upper-limb motor
impairment (Figure 3) shows near normal performance (rating = 2) on five
of the six easiest items and partial performance (rating = 1) on four of
the nine most difficult items. The area boxed by the dashed line in
Figure 3 (i.e., items from movement without tremor to forearm
supination) illustrates the transition zone, in which the patient's
performance fluctuates between partial performance (rating = 1) and near
normal performance (rating = 2). Relative to both the severe and
moderate patients, the individual with mild upper-limb motor impairment
(Figure 4) shows near normal performance (rating = 2) on the vast
majority of the assessment items, with partial performance (rating = 1)
on only the four most difficult items. The area boxed by the dashed line
in Figure 4 illustrates the transition zone, in which the patient's
performance fluctuates between partial performance (rating = 1) and near
normal performance (rating = 2).

[FIGURE 2 OMITTED]

We should note that this overall scoring pattern is based on the
probability of a patient's response to items. That is, there is not
an absolute pattern of first passing all the easy items, then partially
passing all the moderately difficult items, and then failing the next
set of all the most difficult items. For example, the patient with
severe upper-limb motor impairment (Figure 2) was unable to perform
several easier items (e.g., cylindrical grasp, movement without tremor)
while being successful or partially successful on several more
challenging items (e.g., scapular retraction, shoulder external
rotation). Similarly, the patient with moderate upper-limb motor
impairment (Figure 3) fluctuated between partial and near normal
performance on the easiest items, then scored a 0 (unable to perform) on
a relatively easy item (e.g., movement without tremor).

The linear measure of each person can be estimated in each of the
figures. Linacre suggested drawing a vertical line through the
"bulk" of the circled ratings [24]. For example, for the
patient with severe upper-limb motor impairment (Figure 2), the majority
of the ratings are unable to perform (rating = 0) and then fluctuate
between partial performance (rating = 1) and near normal performance
(rating = 2). For the patient with moderate upper-limb motor impairment
(Figure 3), the bulk of the ratings are between partial performance
level (rating = 1) and near normal performance level (rating = 2). For
the patient with mild upper-limb motor impairment (Figure 4), the bulk
of the ratings are at the near normal performance level (rating = 2). In
Figures 2-4, the solid vertical line represents the actual
person-ability measure as derived by Rasch analysis and the dotted
vertical lines represent the 95 percent confidence interval surrounding
that measure.

DISCUSSION

The purpose of this article was to demonstrate how the keyform
recovery map, which was derived from the Rasch analysis of a
standardized assessment, could provide useful information for the
practitioner. The FMA-UE keyform maps the relationship between the
ratings of a patient's upper-limb motor performance and the items
of the FMA-UE. Item ratings show a pattern of what the patient can do,
can partially do, and cannot do. The pattern of item ratings reveals a
transition zone, in which the patient's performance fluctuates
between two ratings, therefore, pointing the way toward appropriate
upper-limb motor recovery therapy goals. Finally, the keyform offers a
means to estimate linear measures of individual patients'
upper-limb motor ability.

[FIGURE 3 OMITTED]

The pattern of item responses is probabilistic, not deterministic.
That is, patients do not pass all the easy items, then partially pass
all the moderate items, and then fail all the most difficult items. As
can be seen in the figures, a response to any given item sometimes does
not fit the overall pattern. These findings support using the
probabilistic Rasch model versus the deterministic Guttman scale model
[18]. These findings also support the observations that variability
exists in human motor performance. That is, while the motor pattern
across individuals has a consistent pattern (i.e., easy items to hard
items), variability in an individual's performance reflects
real-life clinical presentation. In contrast to a total score, the
keyform preserves both the consistency (pattern) and uniqueness
(variability) of an individual's motor performance.

One of the advantages of applying the FMA-UE keyform to clinical
practice is that the item-difficulty hierarchy is empirically derived.
Woodbury et al. demonstrated that 98 percent of their 512-patient sample
supported this item hierarchical structure, and this hierarchy remained
stable across two time points in a 377-patient sample [29,34]. These
findings strongly suggest that the keyform can be applied with
confidence to the larger population of individuals with mild to moderate
cortical stroke. From a clinical perspective, these findings indicate
that the FMA-UE keyform can be used as an evidence-based method to
assist the therapist in planning treatment and monitoring patient
progress.

Ratings plotted on the FMA-UE keyform show a transition zone
occurring above easier items on which patients show near normal
performance (rating = 2) and below more difficult items on which the
patients show partial performance (rating = 1) or the inability to
perform (rating = 0). This transition zone can be the basis for goal
setting and treatment planning. Because items in the transition zone are
at the "just right challenge" level for the individual, the
position of items in relationship to this zone reflect the
patient's expected next steps in the poststroke upper-limb motor
recovery process. Motor behaviors (items) within the transition zone may
suggest appropriate shorter term functional goals, while motor behaviors
(items) above the transition zone may form the foundation for
appropriate longer term functional goals.

[FIGURE 4 OMITTED]

For example, in Figure 3, the patient is transitioning between
partial and near normal ability for motor behaviors (items) such as
elbow extension, shoulder flexion to 90[degrees] with elbow extended,
and shoulder external rotation. Reasonable short-term goals would be
directed at functional activities involving reaching away from the body,
such as bathing (washing/drying body parts), dressing (obtaining
clothing from storage area), and/or job performance (completing
work-related desktop activities).

Figure 3 shows that this patient is having more difficulty with
shoulder abduction to 90[degrees] with elbow extended, wrist
flexion/extension with elbow extended, and wrist circumduction.
Reasonable long-term goals would be directed at functional activities
involving more extreme reach and the coordinated use of multiple joints,
such as dressing (fastening and adjusting clothing and shoes), home
management (yard/garden work), and shopping (selecting and purchasing
items). These treatment goals are by no means intended to be exhaustive.
In the hands of an innovative rehabilitation therapist with excellent
task-analysis skills, the keyform can provide a framework for myriad
patient-tailored goals.

While the ideal of research is to translate findings into practice,
this lofty goal is often inhibited by the scientific methods available.
Rasch methodologies present an innovative way to think about our
standardized assessments. For the first time, a methodology is available
that connects assessment scores and ratings to the qualitative content
of an instrument. This connection not only allows us to reevaluate
traditional expectations of poststroke upper-limb recovery but also maps
recovery as a progression of item difficulties. A premise of this
article is that standardized assessments have offered the practicing
therapist little benefit for making day-to-day clinical practice
decisions. The keyform recovery map provides a framework to facilitate
goal setting and treatment planning by restructuring the assessment
using Rasch analysis. Efforts such as these may show promise for closing
the gap between research and practice in rehabilitation.

LIMITATIONS

There are several limitations to this study. First, regarding
instantaneous measurement, as proposed by Linacre and Kielhofner et al.,
keyforms have considerable error in determining person measures,
especially for individuals at the extremes of the distribution. For
example, for the individual with mild upper-limb motor impairment
(Figure 4), using Linacre's suggestion for determining a
person's measure [24] by placing a vertical line through the
"bulk" of the responses, the rater would likely place the
vertical line to the left of the actual measure (left of the vertical
line depicted in the figure), possibly outside of the 95 percent
confidence interval (dotted vertical lines). This imprecision of the
keyform is a function of increased error at the extremes of the
instrument (i.e., at the ceiling and floor), especially when attempting
to measure individuals of very high or very low ability. Relative to the
individual depicted in Figure 4, if the instrument had more difficult
items (i.e., items for which the individual showed partial performance),
a more accurate placement of the vertical line and determination of the
individual's measure could be achieved.

In addition, as with all statistical values, the location of the
demarcations of the transition zone and decisions for shorter term and
longer term goals is imprecise (i.e., cannot be specified to an
individual item on the item hierarchy). Moreover, the previous
suggestions for interventions are hypothetical. That is, these suggested
interventions have not been applied to clinical populations within
clinical practice. A promising clinical study would be to determine
whether an intervention generated through keyforms is more or less
effective than traditionally derived clinical interventions.

CONCLUSIONS

In summary, keyforms provide an innovative way to immediately apply
the findings from a standardized assessment to clinical practice. This
methodology incorporates evidence-based practice (e.g., empirically
derived FMA-UE item-difficulty hierarchy) with state-of-the-art
measurement theory (i.e., IRT). We should note that the demonstration
given here is not intended to suggest that clinically useful keyforms
can be generated from any assessment. The FMA-UE is a well-developed,
psychometrically sound instrument that supports a logical
item-difficulty hierarchy. Standardized assessments with these
characteristics, or that are designed with these characteristics in
mind, should serve as candidates for creating keyforms that will support
day-to-day clinical practice.

CLINICAL MESSAGE

* Evaluation forms, such as keyforms, may assist therapists in
making day-to-day clinical decisions.

* Keyforms can be created using IRT statistical methods, which
connect the score to patients' performance on specific items.

* Well-developed standardized assessments, especially those with a
logical item-difficulty structure, may be promising candidates for
generating keyforms.

We present a novel way to score a poststroke arm movement
assessment. The scoring form allows a therapist to document whether a
person can do easy, moderately difficult, or very difficult arm
movements. A patient's pattern of scores illustrates how much arm
movement he or she has recovered, specifies which movements are expected
to recover next, and identifies movements that will take longer to
recover. Using the scoring form, a therapist can establish shorter and
longer term treatment goals appropriate for a patient's specific
level of movement ability. This method should be clinically useful
because it connects patient assessment results to patient treatment
plans.

ACKNOWLEDGMENTS

Author Contributions:

Study concept and design: C. A. Velozo, M. L. Woodbury.

Acquisition of data: M. L. Woodbury.

Analysis and interpretation of data: C. A. Velozo, M. L. Woodbury.

Drafting of manuscript: C. A. Velozo, M. L. Woodbury.

Critical revision of manuscript for important intellectual content:

C. A. Velozo, M. L. Woodbury.

Statistical analysis: M. L. Woodbury.

Obtained funding: M. L. Woodbury.

Financial Disclosures: The authors have declared that no competing
interests exist.

Funding/Support: This material is based on work supported by the
Department of Veterans Affairs, Veterans Health Administration, Office
of Research and Development, Rehabilitation Research and Development
Service, Career Development-2 Award (project B6332W), principal
investigator Michelle L. Woodbury.

Institutional Review: The protocol for the study was approved by
the University of Florida Institutional Review Board and was conducted
in a manner that conformed to the approved protocol.

Participant Follow-Up: The authors do not plan to notify
participants of the publication of this study because of a lack of
contact information.

Submitted for publication October 20, 2010. Accepted in revised
form April 4, 2011.

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